Underestimation of N2 O emissions in a comparison of the DayCent, DNDC, and EPIC models.

Abstract

Process-based models are increasingly used to study agroecosystem interactions and N2 O emissions from agricultural fields. The widespread use of these models to conduct research and inform policy benefits from periodic model comparisons that assess the state of agroecosystem modeling and indicate areas for model improvement. This work provides an evaluation of simulated N2 O flux from three process-based models: DayCent, DNDC, and EPIC. The models were calibrated and validated using data collected from two research sites over five years that represent cropping systems and nitrogen fertilizer management strategies common to dairy cropping systems. We also evaluated the use of a multi-model ensemble strategy, which inconsistently outperformed individual model estimations. Regression analysis indicated a cross-model bias to underestimate high magnitude daily and cumulative N2 O flux. Model estimations of observed soil temperature and water content did not sufficiently explain model underestimations, and we found significant variation in model estimates of heterotrophic respiration, denitrification, soil NH4+ , and soil NO3- , which may indicate that additional types of observed data are required to evaluate model performance and possible biases. Our results suggest a bias in the model estimation of N2 O flux from agroecosystems that limits the extension of models beyond calibration and as instruments of policy development. This highlights a growing need for the modeling and measurement communities to collaborate in the collection and analysis of the data necessary to improve models and coordinate future development.